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Li X, Wang F, Xia C, The HL, Bomer JG, Wang Y. Laser Controlled Manipulation of Microbubbles on a Surface with Silica-Coated Gold Nanoparticle Array. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023:e2302939. [PMID: 37496086 DOI: 10.1002/smll.202302939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 07/13/2023] [Indexed: 07/28/2023]
Abstract
Microbubble generation and manipulation play critical roles in diverse applications such as microfluidic mixing, pumping, and microrobot propulsion. However, existing methods are typically limited to lateral movements on customized substrates or rely on specific liquids with particular properties or designed concentration gradients, thereby hindering their practical applications. To address this challenge, this paper presents a method that enables robust vertical manipulation of microbubbles. By focusing a resonant laser on hydrophilic silica-coated gold nanoparticle arrays immersed in water, plasmonic microbubbles are generated and detach from the substrates immediately upon cessation of laser irradiation. Using simple laser pulse control, it can achieve an adjustable size and frequency of bubble bouncing, which is governed by the movement of the three-phase contact line during surface wetting. Furthermore, it demonstrates that rising bubbles can be pulled back by laser irradiation induced thermal Marangoni flow, which is verified by particle image velocimetry measurements and numerical simulations. This study provides novel insights into flexible bubble manipulation and integration in microfluidics, with significant implications for various applications including mixing, drug delivery, and the development of soft actuators.
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Affiliation(s)
- Xiaolai Li
- Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, P. R. China
| | - Fulong Wang
- Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, P. R. China
| | - Chenliang Xia
- Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, P. R. China
| | - Hai Le The
- BIOS Lab-on-a-chip, University of Twente, Enschede, P.O. Box 217, 7500AE, The Netherlands
- Physics of Fluids, Max Planck Center Twente for Complex Fluid Dynamics and J.M. Burgers Centre for Fluid Mechanics, University of Twente, Enschede, P.O. Box 217, 7500AE, The Netherlands
| | - Johan G Bomer
- BIOS Lab-on-a-chip, University of Twente, Enschede, P.O. Box 217, 7500AE, The Netherlands
| | - Yuliang Wang
- Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, P. R. China
- Ningbo Institute of Technology, Beihang University, Ningbo, 315832, P. R. China
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2
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Ding Y, Dhawan G, Jones C, Ness T, Nichols E, Krasnogor N, Reynolds NJ. An open source pipeline for quantitative immunohistochemistry image analysis of inflammatory skin disease using artificial intelligence. J Eur Acad Dermatol Venereol 2023; 37:605-614. [PMID: 36367625 PMCID: PMC10947200 DOI: 10.1111/jdv.18726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 08/17/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND The application of artificial intelligence (AI) to whole slide images has the potential to improve research reliability and ultimately diagnostic efficiency and service capacity. Image annotation plays a key role in AI and digital pathology. However, the work-streams required for tissue-specific (skin) and immunostain-specific annotation has not been extensively studied compared with the development of AI algorithms. OBJECTIVES The objective of this study is to develop a common workflow for annotating whole slide images of biopsies from inflammatory skin disease immunostained with a variety of epidermal and dermal markers prior to the development of the AI-assisted analysis pipeline. METHODS A total of 45 slides containing 3-5 sections each were scanned using Aperio AT2 slide scanner (Leica Biosystems). These slides were annotated by hand using a commonly used image analysis tool which resulted in more than 4000 images blocks. We used deep learning (DL) methodology to first sequentially segment (epidermis and upper dermis), with the exclusion of common artefacts and second to quantify the immunostained signal in those two compartments of skin biopsies and the ratio of positive cells. RESULTS We validated two DL models using 10-fold validation runs and by comparing to ground truth manually annotated data. The models achieved an average (global) accuracy of 95.0% for the segmentation of epidermis and dermis and 86.1% for the segmentation of positive/negative cells. CONCLUSIONS The application of two DL models in sequence facilitates accurate segmentation of epidermal and dermal structures, exclusion of common artefacts and enables the quantitative analysis of the immunostained signal. However, inaccurate annotation of the slides for training the DL model can decrease the accuracy of the output. Our open source code will facilitate further external validation across different immunostaining platforms and slide scanners.
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Affiliation(s)
- Yuchun Ding
- Interdisciplinary Computing and Complex Biosystems Research Group, School of Computing ScienceNewcastle UniversityNewcastle upon TyneUK
| | - Gaurav Dhawan
- Institute of Translational and Clinical MedicineNewcastle University Medical SchoolNewcastle upon TyneUK
- Department of Dermatology, Royal Victoria InfirmaryNewcastle Hospitals NHS Foundation TrustNewcastle upon TyneUK
| | - Claire Jones
- MRC/EPSRC, Molecular Pathology Node, Department of PathologyNewcastle Hospitals NHS Foundation TrustNewcastle upon TyneUK
| | - Thomas Ness
- MRC/EPSRC, Molecular Pathology Node, Department of PathologyNewcastle Hospitals NHS Foundation TrustNewcastle upon TyneUK
| | - Esme Nichols
- Institute of Translational and Clinical MedicineNewcastle University Medical SchoolNewcastle upon TyneUK
- Department of Dermatology, Royal Victoria InfirmaryNewcastle Hospitals NHS Foundation TrustNewcastle upon TyneUK
| | - Natalio Krasnogor
- Interdisciplinary Computing and Complex Biosystems Research Group, School of Computing ScienceNewcastle UniversityNewcastle upon TyneUK
| | - Nick J. Reynolds
- Institute of Translational and Clinical MedicineNewcastle University Medical SchoolNewcastle upon TyneUK
- Department of Dermatology, Royal Victoria InfirmaryNewcastle Hospitals NHS Foundation TrustNewcastle upon TyneUK
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3
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Zhang L, Ma X, Wu Z, Liu J, Gu C, Zhu Z, Wang J, Shu W, Li K, Hu J, Lv X. Prevalence of ground glass nodules in preschool children: a cross-sectional study. Transl Pediatr 2022; 11:1796-1803. [PMID: 36506779 PMCID: PMC9732604 DOI: 10.21037/tp-22-465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/27/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Following increased screening efforts and the use of thin-slice computed tomography (CT), there has been a considerable increase in the incidence of ground-glass nodules (GGNs) in adults. As a result, we have more and more treatments for ground-glass nodules in adults, but few in children. Most think development pattern of pulmonary GGNs is lung inflammation, tumor, or tuberculosis that are more related to acquired or environmental factors. By studying the incidence of pulmonary GGNs in preschool children, we sought to determine whether we had ground glass nodules in the lung before we were teenagers, but we didn't pay attention to them until later. If the hypothesis holds, we may change the cognition and treatment strategies of ground glass nodules. Even not, there are few epidemiological studies with big data that can fill this gap. METHODS We retrospectively collected the data of all preschool children who had undergone CT at the Children's Hospital of Zhejiang University School of Medicine from 2013 to 2020. These data were filtered according to the following exclusion criteria: severe artifacts, data with identical names to the original data; and patients without follow-up records (≥3 months). Inclusion criteria: must have undergone thin-slice CT (≤1.25 mm) at the first and last follow-up. Two thoracic radiologists with 5 years of experience and another senior one assessed the images. RESULTS There were a total of 13,361 cases after relevant exclusions, 311 patients were finally enrolled. Clinical features: age at diagnosis (year): 3.56±1.84, female: 147, male: 164, follow-up interval (month): 6.90±4.74, leukemia: 99, pneumonia: 21, lung cyst: 8, space-occupying lesions outside the lungs: 69, foreign body in respiratory tract: 6. After manual screening and reading, only 1 patient meets all requirements. The results showed that between 2013 and 2020, the incidence of GGNs that could be basically determined in the Children's Hospital of Zhejiang University School of Medicine was 0.32%. CONCLUSIONS There have been few previous studies of GGNs in children, and based on our study, we found that there is still some associated morbidity for preschool children, it is rarely found when they are young.
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Affiliation(s)
- Lichen Zhang
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaohui Ma
- Department of Medical Imaging, the Children's Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zhigang Wu
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiacong Liu
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chen Gu
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ziyue Zhu
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiachuan Wang
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenbo Shu
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Kai Li
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jian Hu
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiayi Lv
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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A Novel Method for Effective Cell Segmentation and Tracking in Phase Contrast Microscopic Images. SENSORS 2021; 21:s21103516. [PMID: 34070081 PMCID: PMC8158140 DOI: 10.3390/s21103516] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/12/2021] [Accepted: 05/14/2021] [Indexed: 11/16/2022]
Abstract
Cell migration plays an important role in the identification of various diseases and physiological phenomena in living organisms, such as cancer metastasis, nerve development, immune function, wound healing, and embryo formulation and development. The study of cell migration with a real-time microscope generally takes several hours and involves analysis of the movement characteristics by tracking the positions of cells at each time interval in the images of the observed cells. Morphological analysis considers the shapes of the cells, and a phase contrast microscope is used to observe the shape clearly. Therefore, we developed a segmentation and tracking method to perform a kinetic analysis by considering the morphological transformation of cells. The main features of the algorithm are noise reduction using a block-matching 3D filtering method, k-means clustering to mitigate the halo signal that interferes with cell segmentation, and the detection of cell boundaries via active contours, which is an excellent way to detect boundaries. The reliability of the algorithm developed in this study was verified using a comparison with the manual tracking results. In addition, the segmentation results were compared to our method with unsupervised state-of-the-art methods to verify the proposed segmentation process. As a result of the study, the proposed method had a lower error of less than 40% compared to the conventional active contour method.
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5
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Mota SM, Rogers RE, Haskell AW, McNeill EP, Kaunas R, Gregory CA, Giger ML, Maitland KC. Automated mesenchymal stem cell segmentation and machine learning-based phenotype classification using morphometric and textural analysis. J Med Imaging (Bellingham) 2021; 8:014503. [PMID: 33542945 PMCID: PMC7849042 DOI: 10.1117/1.jmi.8.1.014503] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 01/11/2021] [Indexed: 01/22/2023] Open
Abstract
Purpose: Mesenchymal stem cells (MSCs) have demonstrated clinically relevant therapeutic effects for treatment of trauma and chronic diseases. The proliferative potential, immunomodulatory characteristics, and multipotentiality of MSCs in monolayer culture is reflected by their morphological phenotype. Standard techniques to evaluate culture viability are subjective, destructive, or time-consuming. We present an image analysis approach to objectively determine morphological phenotype of MSCs for prediction of culture efficacy. Approach: The algorithm was trained using phase-contrast micrographs acquired during the early and mid-logarithmic stages of MSC expansion. Cell regions are localized using edge detection, thresholding, and morphological operations, followed by cell marker identification using H-minima transform within each region to differentiate individual cells from cell clusters. Clusters are segmented using marker-controlled watershed to obtain single cells. Morphometric and textural features are extracted to classify cells based on phenotype using machine learning. Results: Algorithm performance was validated using an independent test dataset of 186 MSCs in 36 culture images. Results show 88% sensitivity and 86% precision for overall cell detection and a mean Sorensen-Dice coefficient of 0.849 ± 0.106 for segmentation per image. The algorithm exhibited an area under the curve of 0.816 (CI 95 = 0.769 to 0.886) and 0.787 (CI 95 = 0.716 to 0.851) for classifying MSCs according to their phenotype at early and mid-logarithmic expansion, respectively. Conclusions: The proposed method shows potential to segment and classify low and moderately dense MSCs based on phenotype with high accuracy and robustness. It enables quantifiable and consistent morphology-based quality assessment for various culture protocols to facilitate cytotherapy development.
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Affiliation(s)
- Sakina M. Mota
- Texas A&M University, Department of Biomedical Engineering, College Station, Texas, United States
| | - Robert E. Rogers
- Texas A&M Health Science Center, College of Medicine, Bryan, Texas, United States
| | - Andrew W. Haskell
- Texas A&M Health Science Center, College of Medicine, Bryan, Texas, United States
| | - Eoin P. McNeill
- Texas A&M Health Science Center, College of Medicine, Bryan, Texas, United States
| | - Roland Kaunas
- Texas A&M University, Department of Biomedical Engineering, College Station, Texas, United States
- Texas A&M Health Science Center, College of Medicine, Bryan, Texas, United States
| | - Carl A. Gregory
- Texas A&M Health Science Center, College of Medicine, Bryan, Texas, United States
| | - Maryellen L. Giger
- University of Chicago, Department of Radiology, Committee on Medical Physics, Chicago, Illinois, United States
| | - Kristen C. Maitland
- Texas A&M University, Department of Biomedical Engineering, College Station, Texas, United States
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6
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Zeng B, Wang Y, Zaytsev ME, Xia C, Zandvliet HJW, Lohse D. Giant plasmonic bubbles nucleation under different ambient pressures. Phys Rev E 2020; 102:063109. [PMID: 33466073 DOI: 10.1103/physreve.102.063109] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 12/08/2020] [Indexed: 11/07/2022]
Abstract
Water-immersed gold nanoparticles irradiated by a laser can trigger the nucleation of plasmonic bubbles after a delay time of a few microseconds [Wang et al., Proc. Natl. Acad. Sci. USA 122, 9253 (2018)]. Here we systematically investigated the light-vapor conversion efficiency, η, of these plasmonic bubbles as a function of the ambient pressure. The efficiency of the formation of these initial-phase and mainly water-vapor containing bubbles, which is defined as the ratio of the energy that is required to form the vapor bubbles and the total energy dumped in the gold nanoparticles before nucleation of the bubble by the laser, can be as high as 25%. The amount of vaporized water first scales linearly with the total laser energy dumped in the gold nanoparticles before nucleation, but for larger energies the amount of vaporized water levels off. The efficiency η decreases with increasing ambient pressure. The experimental observations can be quantitatively understood within a theoretical framework based on the thermal diffusion equation and the thermal dynamics of the phase transition.
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Affiliation(s)
- Binglin Zeng
- School of Mechanical Engineering and Automation, Beihang University, 37 Xueyuan Rd, Haidian District, Beijing, China.,Physics of Fluids Group, Department of Applied Physics and J. M. Burgers Centre for Fluid Dynamics, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands.,Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, 37 Xueyuan Rd, Haidian District, Beijing, China.,Physics of Interfaces and Nanomaterials, MESA+ Institute for Nanotechnology, University of Twente, 7500 AE Enschede, The Netherlands
| | - Yuliang Wang
- School of Mechanical Engineering and Automation, Beihang University, 37 Xueyuan Rd, Haidian District, Beijing, China.,Physics of Fluids Group, Department of Applied Physics and J. M. Burgers Centre for Fluid Dynamics, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands.,Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, 37 Xueyuan Rd, Haidian District, Beijing, China
| | - Mikhail E Zaytsev
- Physics of Fluids Group, Department of Applied Physics and J. M. Burgers Centre for Fluid Dynamics, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands.,Physics of Interfaces and Nanomaterials, MESA+ Institute for Nanotechnology, University of Twente, 7500 AE Enschede, The Netherlands
| | - Chenliang Xia
- School of Mechanical Engineering and Automation, Beihang University, 37 Xueyuan Rd, Haidian District, Beijing, China
| | - Harold J W Zandvliet
- Physics of Interfaces and Nanomaterials, MESA+ Institute for Nanotechnology, University of Twente, 7500 AE Enschede, The Netherlands
| | - Detlef Lohse
- Physics of Fluids Group, Department of Applied Physics and J. M. Burgers Centre for Fluid Dynamics, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands.,Max Planck Institute for Dynamics and Self-Organization, Am Fassberg 17, 37077 Göttingen, Germany
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7
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Belashov AV, Zhikhoreva AA, Belyaeva TN, Kornilova ES, Salova AV, Semenova IV, Vasyutinskii OS. In vitro monitoring of photoinduced necrosis in HeLa cells using digital holographic microscopy and machine learning. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2020; 37:346-352. [PMID: 32118916 DOI: 10.1364/josaa.382135] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 01/03/2020] [Indexed: 06/10/2023]
Abstract
Digital holographic microscopy supplemented with the developed cell segmentation and machine learning and classification algorithms is implemented for quantitative description of the dynamics of cellular necrosis induced by photodynamic treatment in vitro. It is demonstrated that the developed algorithms operating with a set of optical, morphological, and physiological parameters of cells, obtained from their phase images, can be used for automatic distinction between live and necrotic cells. The developed classifier provides high accuracy of about 95.5% and allows for calculation of survival rates in the course of cell death.
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8
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Perez-Ortiz AC, Peralta-Ildefonso MJ, Lira-Romero E, Moya-Albor E, Brieva J, Ramirez-Sanchez I, Clapp C, Luna-Angulo A, Rendon A, Adan-Castro E, Ramírez-Hernández G, Díaz-Lezama N, Coral-Vázquez RM, Estrada-Mena FJ. Lack of Delta-Sarcoglycan ( Sgcd) Results in Retinal Degeneration. Int J Mol Sci 2019; 20:ijms20215480. [PMID: 31689918 PMCID: PMC6862322 DOI: 10.3390/ijms20215480] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 10/31/2019] [Accepted: 10/31/2019] [Indexed: 12/11/2022] Open
Abstract
Age-related macular degeneration (AMD) is the leading cause of central vision loss and severe blindness among the elderly population. Recently, we reported on the association of the SGCD gene (encoding for δ-sarcoglycan) polymorphisms with AMD. However, the functional consequence of Sgcd alterations in retinal degeneration is not known. Herein, we characterized changes in the retina of the Sgcd knocked-out mouse (KO, Sgcd-/-). At baseline, we analyzed the retina structure of three-month-old wild-type (WT, Sgcd+/+) and Sgcd-/- mice by hematoxylin and eosin (H&E) staining, assessed the Sgcd-protein complex (α-, β-, γ-, and ε-sarcoglycan, and sarcospan) by immunofluorescence (IF) and Western blot (WB), and performed electroretinography. Compared to the WT, Sgcd-/- mice are five times more likely to have retinal ruptures. Additionally, all the retinal layers are significantly thinner, more so in the inner plexiform layer (IPL). In addition, the number of nuclei in the KO versus the WT is ever so slightly increased. WT mice express Sgcd-protein partners in specific retinal layers, and as expected, KO mice have decreased or no protein expression, with a significant increase in the α subunit. At three months of age, there were no significant differences in the scotopic electroretinographic responses, regarding both a- and b-waves. According to our data, Sgcd-/- has a phenotype that is compatible with retinal degeneration.
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Affiliation(s)
- Andric C Perez-Ortiz
- Massachusetts General Hospital, Division of Surgery, 55 Fruit St, Boston, MA 02214, USA.
- Laboratory of Epidemiology and Public Health, Yale University School of Public Health, 60 College St, New Haven, CT 06510, USA.
| | - Martha J Peralta-Ildefonso
- Facultad de Química, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Mexico.
- Laboratorio de Biología Molecular, Universidad Panamericana, Escuela de Medicina, Donatello 59 Insurgentes Mixcoac Benito Juárez, 03920 Ciudad de México, Mexico.
| | - Esmeralda Lira-Romero
- Laboratorio de Biología Molecular, Universidad Panamericana, Escuela de Medicina, Donatello 59 Insurgentes Mixcoac Benito Juárez, 03920 Ciudad de México, Mexico.
| | - Ernesto Moya-Albor
- Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, 03920 Ciudad de México, Mexico.
| | - Jorge Brieva
- Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, 03920 Ciudad de México, Mexico.
| | - Israel Ramirez-Sanchez
- Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, 11340 Ciudad de México, Mexico.
| | - Carmen Clapp
- Instituto de Neurobiología, Campus UNAM-Juriquilla, Universidad Nacional Autónoma de México (UNAM), 76230 Querétaro, Mexico.
| | - Alexandra Luna-Angulo
- Departamento de Neurociencias, Instituto Nacional de rehabilitación, México-Xochimilco, No.289. Arenal de Guadalupe, 14389 Ciudad de México, Mexico.
| | - Alvaro Rendon
- Institut De La Vision, Sorbonne Universites, F-75012 Paris, France.
| | - Elva Adan-Castro
- Instituto de Neurobiología, Campus UNAM-Juriquilla, Universidad Nacional Autónoma de México (UNAM), 76230 Querétaro, Mexico.
| | - Gabriela Ramírez-Hernández
- Instituto de Neurobiología, Campus UNAM-Juriquilla, Universidad Nacional Autónoma de México (UNAM), 76230 Querétaro, Mexico.
| | - Nundehui Díaz-Lezama
- Department of Physiological Genomics, Ludwig-Maximilians-Universität München, Großhaderner Str. 9, 82152 Planegg-Martinsried, Germany.
| | - Ramón M Coral-Vázquez
- Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, 11340 Ciudad de México, Mexico.
- Subdirección de Enseñanza e Investigación, Centro Médico Nacional "20 de Noviembre", Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado, 03100 Ciudad de México, Mexico.
| | - Francisco J Estrada-Mena
- Laboratorio de Biología Molecular, Universidad Panamericana, Escuela de Medicina, Donatello 59 Insurgentes Mixcoac Benito Juárez, 03920 Ciudad de México, Mexico.
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9
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Li X, Wang Y, Zaytsev ME, Lajoinie G, Le The H, Bomer JG, Eijkel JCT, Zandvliet HJW, Zhang X, Lohse D. Plasmonic Bubble Nucleation and Growth in Water: Effect of Dissolved Air. THE JOURNAL OF PHYSICAL CHEMISTRY. C, NANOMATERIALS AND INTERFACES 2019; 123:23586-23593. [PMID: 31583035 PMCID: PMC6768170 DOI: 10.1021/acs.jpcc.9b05374] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 08/23/2019] [Indexed: 05/05/2023]
Abstract
Under continuous laser irradiation, noble metal nanoparticles immersed in water can quickly heat up, leading to the nucleation of so-called plasmonic bubbles. In this work, we want to further understand the bubble nucleation and growth mechanism. In particular, we quantitatively study the effect of the amount of dissolved air on the bubble nucleation and growth dynamics, both for the initial giant bubble, which forms shortly after switching on the laser and is mainly composed of vapor, and for the final life phase of the bubble, during which it mainly contains air expelled from water. We found that the bubble nucleation temperature depends on the gas concentration: the higher the gas concentration, the lower the bubble nucleation temperature. Also, the long-term diffusion-dominated bubble growth is governed by the gas concentration. The radius of the bubbles grows as R(t) ∝ t 1/3 for air-equilibrated and air-oversaturated water. In contrast, in partially degassed water, the growth is much slower since, even for the highest temperature we achieve, the water remains undersaturated.
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Affiliation(s)
- Xiaolai Li
- Physics
of Fluids, Max Planck Center Twente for Complex Fluid Dynamics
and J.M. Burgers Centre for Fluid Mechanics, MESA+ Institute, Physics of Interfaces
and Nanomaterials, MESA+ Institute, TechMed Centre, and BIOS Lab-on-a-Chip, MESA+ Institute, University of Twente, P.O. Box 217, 7500AE Enschede, The Netherlands
- Robotics Institute,
School of Mechanical Engineering and Automation and Beijing Advanced Innovation
Center for Biomedical Engineering, Beihang
University, 37 Xueyuan Road, Haidian District, Beijing 100191, P.R. China
| | - Yuliang Wang
- Robotics Institute,
School of Mechanical Engineering and Automation and Beijing Advanced Innovation
Center for Biomedical Engineering, Beihang
University, 37 Xueyuan Road, Haidian District, Beijing 100191, P.R. China
- E-mail: (Y.W.)
| | - Mikhail E. Zaytsev
- Physics
of Fluids, Max Planck Center Twente for Complex Fluid Dynamics
and J.M. Burgers Centre for Fluid Mechanics, MESA+ Institute, Physics of Interfaces
and Nanomaterials, MESA+ Institute, TechMed Centre, and BIOS Lab-on-a-Chip, MESA+ Institute, University of Twente, P.O. Box 217, 7500AE Enschede, The Netherlands
| | - Guillaume Lajoinie
- Physics
of Fluids, Max Planck Center Twente for Complex Fluid Dynamics
and J.M. Burgers Centre for Fluid Mechanics, MESA+ Institute, Physics of Interfaces
and Nanomaterials, MESA+ Institute, TechMed Centre, and BIOS Lab-on-a-Chip, MESA+ Institute, University of Twente, P.O. Box 217, 7500AE Enschede, The Netherlands
| | - Hai Le The
- Physics
of Fluids, Max Planck Center Twente for Complex Fluid Dynamics
and J.M. Burgers Centre for Fluid Mechanics, MESA+ Institute, Physics of Interfaces
and Nanomaterials, MESA+ Institute, TechMed Centre, and BIOS Lab-on-a-Chip, MESA+ Institute, University of Twente, P.O. Box 217, 7500AE Enschede, The Netherlands
| | - Johan G. Bomer
- Physics
of Fluids, Max Planck Center Twente for Complex Fluid Dynamics
and J.M. Burgers Centre for Fluid Mechanics, MESA+ Institute, Physics of Interfaces
and Nanomaterials, MESA+ Institute, TechMed Centre, and BIOS Lab-on-a-Chip, MESA+ Institute, University of Twente, P.O. Box 217, 7500AE Enschede, The Netherlands
| | - Jan C. T. Eijkel
- Physics
of Fluids, Max Planck Center Twente for Complex Fluid Dynamics
and J.M. Burgers Centre for Fluid Mechanics, MESA+ Institute, Physics of Interfaces
and Nanomaterials, MESA+ Institute, TechMed Centre, and BIOS Lab-on-a-Chip, MESA+ Institute, University of Twente, P.O. Box 217, 7500AE Enschede, The Netherlands
| | - Harold J. W. Zandvliet
- Physics
of Fluids, Max Planck Center Twente for Complex Fluid Dynamics
and J.M. Burgers Centre for Fluid Mechanics, MESA+ Institute, Physics of Interfaces
and Nanomaterials, MESA+ Institute, TechMed Centre, and BIOS Lab-on-a-Chip, MESA+ Institute, University of Twente, P.O. Box 217, 7500AE Enschede, The Netherlands
| | - Xuehua Zhang
- Physics
of Fluids, Max Planck Center Twente for Complex Fluid Dynamics
and J.M. Burgers Centre for Fluid Mechanics, MESA+ Institute, Physics of Interfaces
and Nanomaterials, MESA+ Institute, TechMed Centre, and BIOS Lab-on-a-Chip, MESA+ Institute, University of Twente, P.O. Box 217, 7500AE Enschede, The Netherlands
- Department
of Chemical and Materials Engineering, Donadeo Innovation Centre for
Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada
| | - Detlef Lohse
- Physics
of Fluids, Max Planck Center Twente for Complex Fluid Dynamics
and J.M. Burgers Centre for Fluid Mechanics, MESA+ Institute, Physics of Interfaces
and Nanomaterials, MESA+ Institute, TechMed Centre, and BIOS Lab-on-a-Chip, MESA+ Institute, University of Twente, P.O. Box 217, 7500AE Enschede, The Netherlands
- Max
Planck Institute for Dynamics and Self-Organization, Am Fassberg 17, 37077 Göttingen, Germany
- E-mail: (D.L.)
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10
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Split and Merge Watershed: a two-step method for cell segmentation in fluorescence microscopy images. Biomed Signal Process Control 2019; 53. [PMID: 33719364 DOI: 10.1016/j.bspc.2019.101575] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The development of advanced techniques in medical imaging has allowed scanning of the human body to microscopic levels, making research on cell behavior more complex and more in-depth. Recent studies have focused on cellular heterogeneity since cell-to-cell differences are always present in the cell population and this variability contains valuable information. However, identifying each cell is not an easy task because, in the images acquired from the microscope, there are clusters of cells that are touching one another. Therefore, the segmentation stage is a problem of considerable difficulty in cell image processing. Although several methods for cell segmentation are described in the literature, they have drawbacks in terms of over-segmentation, under-segmentation or misidentification. Consequently, our main motivation in studying cell segmentation was to develop a new method to achieve a good tradeoff between accurately identifying all relevant elements and not inserting segmentation artifacts. This article presents a new method for cell segmentation in fluorescence microscopy images. The proposed approach combines the well-known Marker-Controlled Watershed algorithm (MC-Watershed) with a new, two-step method based on Watershed, Split and Merge Watershed (SM-Watershed): in the first step, or split phase, the algorithm identifies the clusters using inherent characteristics of the cell, such as size and convexity, and separates them using watershed. In the second step, or the merge stage, it identifies the over-segmented regions using proper features of the cells and eliminates the divisions. Before applying our two-step method, the input image is first preprocessed, and the MC-Watershed algorithm is used to generate an initial segmented image. However, this initial result may not be suitable for subsequent tasks, such as cell count or feature extraction, because not all cells are separated, and some cells may be mistakenly confused with the background. Thus, our proposal corrects this issue with its two-step process, reaching a high performance, a suitable tradeoff between over-segmentation and under-segmentation and preserving the shape of the cell, without the need of any labeled data or relying on machine learning processes. The latter is advantageous over state-of-the-art techniques that in order to achieve similar results require labeled data, which may not be available for all of the domains. Two cell datasets were used to validate this approach, and the results were compared with other methods in the literature, using traditional metrics and quality visual assessment. We obtained 90% of average visual accuracy and an F-index higher than 80%. This proposal outperforms other techniques for cell separation, achieving an acceptable balance between over-segmentation and under-segmentation, which makes it suitable for several applications in cell identification, such as virus infection analysis, high-content cell screening, drug discovery, and morphometry.
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11
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Disrupted alternative splicing for genes implicated in splicing and ciliogenesis causes PRPF31 retinitis pigmentosa. Nat Commun 2018; 9:4234. [PMID: 30315276 PMCID: PMC6185938 DOI: 10.1038/s41467-018-06448-y] [Citation(s) in RCA: 133] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 09/03/2018] [Indexed: 12/23/2022] Open
Abstract
Mutations in pre-mRNA processing factors (PRPFs) cause autosomal-dominant retinitis pigmentosa (RP), but it is unclear why mutations in ubiquitously expressed genes cause non-syndromic retinal disease. Here, we generate transcriptome profiles from RP11 (PRPF31-mutated) patient-derived retinal organoids and retinal pigment epithelium (RPE), as well as Prpf31+/− mouse tissues, which revealed that disrupted alternative splicing occurred for specific splicing programmes. Mis-splicing of genes encoding pre-mRNA splicing proteins was limited to patient-specific retinal cells and Prpf31+/− mouse retinae and RPE. Mis-splicing of genes implicated in ciliogenesis and cellular adhesion was associated with severe RPE defects that include disrupted apical – basal polarity, reduced trans-epithelial resistance and phagocytic capacity, and decreased cilia length and incidence. Disrupted cilia morphology also occurred in patient-derived photoreceptors, associated with progressive degeneration and cellular stress. In situ gene editing of a pathogenic mutation rescued protein expression and key cellular phenotypes in RPE and photoreceptors, providing proof of concept for future therapeutic strategies. Mutations in pre-mRNA processing factors cause autosomal dominant retinitis pigmentosa. Here the authors provide insights into the pathophysiological mechanisms underlying non-syndromic retinal disease caused by heterozygous mutations in genes encoding ubiquitously expressed splicing factors.
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12
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Ju M, Choi Y, Seo J, Sa J, Lee S, Chung Y, Park D. A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring. SENSORS 2018; 18:s18061746. [PMID: 29843479 PMCID: PMC6021839 DOI: 10.3390/s18061746] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 05/23/2018] [Accepted: 05/27/2018] [Indexed: 02/06/2023]
Abstract
Segmenting touching-pigs in real-time is an important issue for surveillance cameras intended for the 24-h tracking of individual pigs. However, methods to do so have not yet been reported. We particularly focus on the segmentation of touching-pigs in a crowded pig room with low-contrast images obtained using a Kinect depth sensor. We reduce the execution time by combining object detection techniques based on a convolutional neural network (CNN) with image processing techniques instead of applying time-consuming operations, such as optimization-based segmentation. We first apply the fastest CNN-based object detection technique (i.e., You Only Look Once, YOLO) to solve the separation problem for touching-pigs. If the quality of the YOLO output is not satisfied, then we try to find the possible boundary line between the touching-pigs by analyzing the shape. Our experimental results show that this method is effective to separate touching-pigs in terms of both accuracy (i.e., 91.96%) and execution time (i.e., real-time execution), even with low-contrast images obtained using a Kinect depth sensor.
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Affiliation(s)
- Miso Ju
- Department of Computer Convergence Software, Korea University, Sejong City 30019, Korea.
| | - Younchang Choi
- Department of Computer Convergence Software, Korea University, Sejong City 30019, Korea.
| | - Jihyun Seo
- Department of Computer Convergence Software, Korea University, Sejong City 30019, Korea.
| | - Jaewon Sa
- Department of Computer Convergence Software, Korea University, Sejong City 30019, Korea.
| | - Sungju Lee
- Department of Computer Convergence Software, Korea University, Sejong City 30019, Korea.
| | - Yongwha Chung
- Department of Computer Convergence Software, Korea University, Sejong City 30019, Korea.
| | - Daihee Park
- Department of Computer Convergence Software, Korea University, Sejong City 30019, Korea.
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13
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Wang Y, Lu T, Li X, Wang H. Automated image segmentation-assisted flattening of atomic force microscopy images. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2018; 9:975-985. [PMID: 29719750 PMCID: PMC5905267 DOI: 10.3762/bjnano.9.91] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 02/23/2018] [Indexed: 05/11/2023]
Abstract
Atomic force microscopy (AFM) images normally exhibit various artifacts. As a result, image flattening is required prior to image analysis. To obtain optimized flattening results, foreground features are generally manually excluded using rectangular masks in image flattening, which is time consuming and inaccurate. In this study, a two-step scheme was proposed to achieve optimized image flattening in an automated manner. In the first step, the convex and concave features in the foreground were automatically segmented with accurate boundary detection. The extracted foreground features were taken as exclusion masks. In the second step, data points in the background were fitted as polynomial curves/surfaces, which were then subtracted from raw images to get the flattened images. Moreover, sliding-window-based polynomial fitting was proposed to process images with complex background trends. The working principle of the two-step image flattening scheme were presented, followed by the investigation of the influence of a sliding-window size and polynomial fitting direction on the flattened images. Additionally, the role of image flattening on the morphological characterization and segmentation of AFM images were verified with the proposed method.
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Affiliation(s)
- Yuliang Wang
- School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P.R. China
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, P.R. China
| | - Tongda Lu
- School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P.R. China
| | - Xiaolai Li
- School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P.R. China
| | - Huimin Wang
- Department of Materials Science and Engineering, Ohio State University, 2041 College Rd., Columbus, OH 43210, USA
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14
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Wang Y, Wang C, Zhang Z. Segmentation of clustered cells in negative phase contrast images with integrated light intensity and cell shape information. J Microsc 2017; 270:188-199. [PMID: 29280132 DOI: 10.1111/jmi.12673] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Revised: 10/01/2017] [Accepted: 11/27/2017] [Indexed: 11/28/2022]
Abstract
Automated cell segmentation plays a key role in characterisations of cell behaviours for both biology research and clinical practices. Currently, the segmentation of clustered cells still remains as a challenge and is the main reason for false segmentation. In this study, the emphasis was put on the segmentation of clustered cells in negative phase contrast images. A new method was proposed to combine both light intensity and cell shape information through the construction of grey-weighted distance transform (GWDT) within preliminarily segmented areas. With the constructed GWDT, the clustered cells can be detected and then separated with a modified region skeleton-based method. Moreover, a contour expansion operation was applied to get optimised detection of cell boundaries. In this paper, the working principle and detailed procedure of the proposed method are described, followed by the evaluation of the method on clustered cell segmentation. Results show that the proposed method achieves an improved performance in clustered cell segmentation compared with other methods, with 85.8% and 97.16% accuracy rate for clustered cells and all cells, respectively.
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Affiliation(s)
- Y Wang
- School of Mechanical Engineering and Automation, Robotics Institute, Beihang University, Beijing, China
| | - C Wang
- School of Mechanical Engineering and Automation, Robotics Institute, Beihang University, Beijing, China
| | - Z Zhang
- Université de Bordeaux & CNRS, LOMA, Talence, France
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15
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Jin D, Sung Y, Lue N, Kim YH, So PTC, Yaqoob Z. Large population cell characterization using quantitative phase cytometer. Cytometry A 2017; 91:450-459. [PMID: 28444998 DOI: 10.1002/cyto.a.23106] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Revised: 03/12/2017] [Accepted: 03/15/2017] [Indexed: 11/09/2022]
Abstract
A major challenge in cellular analysis is the phenotypic characterization of large cell populations within a short period of time. Among various parameters for cell characterization, the cell dry mass is often used to describe cell size but is difficult to be measured directly with traditional techniques. Here, we propose an interferometric approach based on line-focused beam illumination for high-content precision dry mass measurements of adherent cells in a non-invasive fashion-we call it quantitative phase cytometry (QPC). Besides dry mass, abundant cellular morphological features such as projected area, sphericity, and phase skewness can be readily extracted from the QPC interferometric data. To validate the utility of our technique, we demonstrate characterizing a large population of ∼104 HeLa cells. Our reported QPC system is envisioned as a promising quantitative tool for label-free characterization of a large cell count at single cell resolution. © 2017 International Society for Advancement of Cytometry.
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Affiliation(s)
- Di Jin
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139.,Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139
| | - Yongjin Sung
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139.,College of Engineering and Applied Sciences, University of Wisconsin, Milwaukee, Wisconsin, 53201
| | - Niyom Lue
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139
| | - Yang-Hyo Kim
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139
| | - Peter T C So
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139.,Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139
| | - Zahid Yaqoob
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139
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16
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Lalli ML, Wojeski B, Asthagiri AR. Label-Free Automated Cell Tracking: Analysis of the Role of E-cadherin Expression in Collective Electrotaxis. Cell Mol Bioeng 2017; 10:89-101. [PMID: 31719851 PMCID: PMC6816619 DOI: 10.1007/s12195-016-0471-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Accepted: 10/14/2016] [Indexed: 12/14/2022] Open
Abstract
Collective cell migration plays an important role in wound healing, organogenesis, and the progression of metastatic disease. Analysis of collective migration typically involves laborious and time-consuming manual tracking of individual cells within cell clusters over several dozen or hundreds of frames. Herein, we develop a label-free, automated algorithm to identify and track individual epithelial cells within a free-moving cluster. We use this algorithm to analyze the effects of partial E-cadherin knockdown on collective migration of MCF-10A breast epithelial cells directed by an electric field. Our data show that E-cadherin knockdown in free-moving cell clusters diminishes electrotactic potential, with empty vector MCF-10A cells showing 16% higher directedness than cells with E-cadherin knockdown. Decreased electrotaxis is also observed in isolated cells at intermediate electric fields, suggesting an adhesion-independent role of E-cadherin in regulating electrotaxis. In additional support of an adhesion-independent role of E-cadherin, isolated cells with reduced E-cadherin expression reoriented within an applied electric field 60% more quickly than control. These results have implications for the role of E-cadherin expression in electrotaxis and demonstrate proof-of-concept of an automated algorithm that is broadly applicable to the analysis of collective migration in a wide range of physiological and pathophysiological contexts.
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Affiliation(s)
- Mark L. Lalli
- Department of Chemical Engineering, Northeastern University, 360 Huntington Ave., Boston, MA 02115 USA
| | - Brooke Wojeski
- Department of Chemical Engineering, Northeastern University, 360 Huntington Ave., Boston, MA 02115 USA
| | - Anand R. Asthagiri
- Department of Chemical Engineering, Northeastern University, 360 Huntington Ave., Boston, MA 02115 USA
- Department of Bioengineering, Northeastern University, Boston, MA USA
- Department of Biology, Northeastern University, Boston, MA USA
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